However, value factorization is prone to a learning pathology called relative overgeneralization [1]} where policies erroeneously converge to a suboptimal joint action.
Relative overgeneralisation commonly arises when multiple agents must coordinate their actions but receive negative rewards if only a subset of them do so.
In this case, random occurrences of such successful coordination events are a needle in a haystack and monotonic value factorisations cannot represent the non-monotonic team reward function [2]}.
A variety of recent approaches employ joint optimisation of, or transfer learning between, factored and unrestricted joint value functions in order to overcome the representational limitations of factored value functions, with varying success [3]}, [4]}.
